AutoML Leaderboard

Best model	name	model_type	metric_type	metric_value	train_time	single_prediction_time
	1_Linear	Linear	average_precision	0.981151	37.12	0.0285
	2_Default_LightGBM	LightGBM	average_precision	0.984335	33.85	0.0133
	3_Default_Xgboost	Xgboost	average_precision	0.979549	33.35	0.0228
	4_Default_CatBoost	CatBoost	average_precision	0.987349	34.17	0.0333
	5_Default_NeuralNetwork	Neural Network	average_precision	0.971355	32.2	0.0405
	6_Default_RandomForest	Random Forest	average_precision	0.978456	35.77	0.1487
	11_LightGBM	LightGBM	average_precision	0.984562	34.78	0.0148
	7_Xgboost	Xgboost	average_precision	0.981538	35.11	0.0137
	15_CatBoost	CatBoost	average_precision	0.988065	35.58	0.0174
	19_RandomForest	Random Forest	average_precision	0.987365	38.27	0.1872
	23_NeuralNetwork	Neural Network	average_precision	0.978522	32.79	0.0313
	12_LightGBM	LightGBM	average_precision	0.981605	34.55	0.0182
	8_Xgboost	Xgboost	average_precision	0.979431	33.48	0.0192
	16_CatBoost	CatBoost	average_precision	0.981597	36.16	0.0246
	20_RandomForest	Random Forest	average_precision	0.984126	37.24	0.1341
	24_NeuralNetwork	Neural Network	average_precision	0.976907	34.78	0.037
	13_LightGBM	LightGBM	average_precision	0.985243	35.9	0.0128
	9_Xgboost	Xgboost	average_precision	0.911635	34.94	0.0144
	17_CatBoost	CatBoost	average_precision	0.980034	36.34	0.0179
	21_RandomForest	Random Forest	average_precision	0.968446	39.55	0.1381
	25_NeuralNetwork	Neural Network	average_precision	0.969369	34.73	0.0367
	14_LightGBM	LightGBM	average_precision	0.982671	36.43	0.0127
	10_Xgboost	Xgboost	average_precision	0.500365	33.86	0.0145
	18_CatBoost	CatBoost	average_precision	0.985217	36.49	0.0169
	22_RandomForest	Random Forest	average_precision	0.973307	38.21	0.1672
	26_NeuralNetwork	Neural Network	average_precision	0.97895	34.08	0.0343
	15_CatBoost_GoldenFeatures	CatBoost	average_precision	0.987348	39.45	0.0404
	19_RandomForest_GoldenFeatures	Random Forest	average_precision	0.981939	40.23	0.1546
	4_Default_CatBoost_GoldenFeatures	CatBoost	average_precision	0.989526	37.73	0.0374
	27_CatBoost_GoldenFeatures	CatBoost	average_precision	0.99044	37.09	0.0348
	28_CatBoost	CatBoost	average_precision	0.97874	37.51	0.013
	29_CatBoost	CatBoost	average_precision	0.990262	38.18	0.0183
	30_RandomForest	Random Forest	average_precision	0.986077	40.24	0.1715
	31_RandomForest	Random Forest	average_precision	0.985326	40.35	0.1417
	32_LightGBM	LightGBM	average_precision	0.985875	37.62	0.0113
	33_LightGBM	LightGBM	average_precision	0.984718	37.79	0.0159
	34_LightGBM	LightGBM	average_precision	0.9859	37.05	0.0113
	35_CatBoost_GoldenFeatures	CatBoost	average_precision	0.989239	38.26	0.0364
	36_CatBoost_GoldenFeatures	CatBoost	average_precision	0.986614	39.03	0.0388
	37_CatBoost	CatBoost	average_precision	0.985173	38.25	0.0144
	38_CatBoost	CatBoost	average_precision	0.981361	39.14	0.0157
the best	Ensemble	Ensemble	average_precision	0.992985	2.69	0.1873

	score	threshold
logloss	0.693206	nan
auc	0.500868	nan
f1	0.666667	0.452935
accuracy	0.502183	0.505075
precision	0.501818	0.505075
recall	1	0.452935
mcc	0.00445767	0.505075

	score	threshold
logloss	0.693206	nan
auc	0.500868	nan
f1	0.547619	0.505075
accuracy	0.502183	0.505075
precision	0.501818	0.505075
recall	0.60262	0.505075
mcc	0.00445767	0.505075

	score	threshold
logloss	0.195958	nan
auc	0.980778	nan
f1	0.955947	0.533395
accuracy	0.956332	0.533395
precision	1	0.928398
recall	1	0.00380183
mcc	0.912803	0.533395

	score	threshold
logloss	0.195958	nan
auc	0.980778	nan
f1	0.955947	0.533395
accuracy	0.956332	0.533395
precision	0.964444	0.533395
recall	0.947598	0.533395
mcc	0.912803	0.533395

	Predicted as 0	Predicted as 1
Labeled as 0	221	8
Labeled as 1	12	217

	score	threshold
logloss	0.156221	nan
auc	0.97992	nan
f1	0.960699	0.451329
accuracy	0.960699	0.451329
precision	1	0.99819
recall	1	1.13086e-05
mcc	0.922665	0.718673

	score	threshold
logloss	0.18233	nan
auc	0.982418	nan
f1	0.958785	0.452312
accuracy	0.958515	0.452312
precision	1	0.928244
recall	1	2.76086e-05
mcc	0.917109	0.452312

	score	threshold
logloss	0.160448	nan
auc	0.978757	nan
f1	0.964758	0.523377
accuracy	0.965066	0.523377
precision	1	0.986192
recall	1	0.00049015
mcc	0.930273	0.523377

	score	threshold
logloss	0.277206	nan
auc	0.986366	nan
f1	0.964758	0.466563
accuracy	0.965066	0.466563
precision	1	0.769136
recall	1	0.0228478
mcc	0.930273	0.466563

	score	threshold
logloss	0.152502	nan
auc	0.984783	nan
f1	0.960699	0.339953
accuracy	0.960699	0.339953
precision	1	0.956087
recall	1	0.000692652
mcc	0.921538	0.386385

	score	threshold
logloss	0.259149	nan
auc	0.978185	nan
f1	0.955947	0.453944
accuracy	0.956332	0.453944
precision	1	0.934871
recall	1	0.000595822
mcc	0.912803	0.453944

	score	threshold
logloss	0.270773	nan
auc	0.978719	nan
f1	0.955947	0.465674
accuracy	0.956332	0.465674
precision	1	0.930674
recall	1	0.000994834
mcc	0.913919	0.497215

	score	threshold
logloss	0.209303	nan
auc	0.983896	nan
f1	0.969432	0.388947
accuracy	0.969432	0.388947
precision	1	0.900033
recall	1	0.000418861
mcc	0.938865	0.388947

	score	threshold
logloss	0.206201	nan
auc	0.984726	nan
f1	0.955947	0.522409
accuracy	0.956332	0.522409
precision	1	0.85675
recall	1	0.0194263
mcc	0.913221	0.56226

	score	threshold
logloss	0.217276	nan
auc	0.976726	nan
f1	0.936937	0.62466
accuracy	0.938865	0.62466
precision	1	0.74639
recall	1	0.00407834
mcc	0.879374	0.62466

	score	threshold
logloss	0.192042	nan
auc	0.974028	nan
f1	0.941176	0.538017
accuracy	0.943231	0.538017
precision	1	0.88594
recall	1	0.00697453
mcc	0.888635	0.538017

	score	threshold
logloss	0.192042	nan
auc	0.974028	nan
f1	0.941176	0.538017
accuracy	0.943231	0.538017
precision	0.976526	0.538017
recall	0.908297	0.538017
mcc	0.888635	0.538017

feature	Learner_1	Learner_2	Learner_3	Learner_4	Learner_5
PEER_PRESSURE	2.09855	1.97751	2.10596	2.24214	2.11845
ALLERGY	1.71851	1.8115	1.80494	1.6434	1.99987
CHRONIC DISEASE	1.87657	1.63242	1.72145	1.71654	1.80335
YELLOW_FINGERS	1.60769	1.59401	1.57961	1.70178	1.69653
SWALLOWING DIFFICULTY	1.54115	1.39416	1.52935	1.57504	1.50732
COUGHING	1.6269	1.44695	1.61924	1.30983	1.45889
WHEEZING	1.39098	1.33944	1.35351	1.6218	1.23305
ALCOHOL CONSUMING	1.2172	1.51097	1.01974	1.43756	1.6208
FATIGUE	1.22949	1.23666	1.24562	1.05542	1.27239
ANXIETY	0.676465	1.11655	0.820949	1.03973	0.927658
CHEST PAIN	0.451082	0.609851	0.301484	0.389733	0.68452
SMOKING	0.404751	0.352542	0.398707	0.489795	0.317827
SHORTNESS OF BREATH	0.222579	0.360146	0.254631	0.399099	0.113896
AGE	-0.0683001	0.0651653	0.0293103	-0.0196802	0.14053
GENDER	-0.0705105	-0.180772	-0.240995	-0.128562	0.147742
intercept	-4.88958	-5.06286	-4.78165	-5.16035	-5.28215

	score	threshold
logloss	0.239429	nan
auc	0.980273	nan
f1	0.933628	0.568717
accuracy	0.934498	0.568717
precision	1	0.659086
recall	1	0
mcc	0.872329	0.610506

	score	threshold
logloss	0.287469	nan
auc	0.963864	nan
f1	0.902386	0.594454
accuracy	0.901747	0.594454
precision	1	0.73422
recall	1	0.0325234
mcc	0.803562	0.594454

	score	threshold
logloss	0.292899	nan
auc	0.968851	nan
f1	0.912134	0.579257
accuracy	0.908297	0.579257
precision	1	0.719996
recall	1	0
mcc	0.819726	0.579257

	score	threshold
logloss	0.232964	nan
auc	0.976917	nan
f1	0.944321	0.492837
accuracy	0.945415	0.492837
precision	1	0.99338
recall	1	6.41845e-12
mcc	0.891518	0.492837

	score	threshold
logloss	0.192402	nan
auc	0.974066	nan
f1	0.944321	0.509772
accuracy	0.945415	0.509772
precision	1	0.999026
recall	1	0.000268946
mcc	0.891518	0.509772

	score	threshold
logloss	0.244839	nan
auc	0.966877	nan
f1	0.938865	0.505872
accuracy	0.938865	0.505872
precision	1	0.991168
recall	1	4.76261e-06
mcc	0.877863	0.551479

	score	threshold
logloss	0.190663	nan
auc	0.979119	nan
f1	0.947598	0.46477
accuracy	0.947598	0.46477
precision	1	0.999892
recall	1	4.76882e-06
mcc	0.895197	0.46477

	score	threshold
logloss	0.133128	nan
auc	0.987186	nan
f1	0.962801	0.40466
accuracy	0.962882	0.40466
precision	1	0.91199
recall	1	0.00263693
mcc	0.925773	0.40466

	score	threshold
logloss	0.304858	nan
auc	0.979196	nan
f1	0.95671	0.456
accuracy	0.956332	0.456
precision	1	0.85841
recall	1	0.0111083
mcc	0.913221	0.505393

AutoML Leaderboard

AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of 10_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

Metric details

Metric details with threshold from accuracy metric

Confusion matrix (at threshold=0.505075)

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

Summary of 11_LightGBM

LightGBM

Validation

Optimized metric

Training time

Metric details

Metric details with threshold from accuracy metric

Confusion matrix (at threshold=0.533395)

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

Summary of 12_LightGBM

LightGBM

Validation

Optimized metric

Training time

Metric details

Metric details with threshold from accuracy metric

Confusion matrix (at threshold=0.451329)

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

Summary of 13_LightGBM

LightGBM

Validation

Optimized metric

Training time

Metric details

Metric details with threshold from accuracy metric

Confusion matrix (at threshold=0.452312)

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

Summary of 14_LightGBM

LightGBM

Validation

	score	threshold
logloss	0.407641	nan
auc	0.987939	nan
f1	0.969298	0.478908
accuracy	0.969432	0.478908
precision	1	0.601983
recall	1	0.100235
mcc	0.939008	0.487179

	score	threshold
logloss	0.200297	nan
auc	0.980283	nan
f1	0.955947	0.487291
accuracy	0.956332	0.487291
precision	1	0.921339
recall	1	0.000282286
mcc	0.912803	0.487291

	score	threshold
logloss	0.193748	nan
auc	0.983343	nan
f1	0.951965	0.480871
accuracy	0.951965	0.480871
precision	1	0.897608
recall	1	0.0198381
mcc	0.90393	0.480871

	score	threshold
logloss	0.240876	nan
auc	0.982037	nan
f1	0.946188	0.586479
accuracy	0.947598	0.586479
precision	1	0.783775
recall	1	0.0184963
mcc	0.89739	0.605179

	score	threshold
logloss	0.218303	nan
auc	0.982485	nan
f1	0.95671	0.479353
accuracy	0.956332	0.479353
precision	1	0.88041
recall	1	0.000316717
mcc	0.913221	0.528092

	score	threshold
logloss	0.200515	nan
auc	0.982628	nan
f1	0.960699	0.469438
accuracy	0.960699	0.469438
precision	1	0.925408
recall	1	0.000141272
mcc	0.921397	0.469438

	score	threshold
logloss	0.183978	nan
auc	0.982323	nan
f1	0.952381	0.417404
accuracy	0.951965	0.417404
precision	1	0.91976
recall	1	0.00294066
mcc	0.904482	0.537173

	score	threshold
logloss	0.136098	nan
auc	0.985183	nan
f1	0.965066	0.414042
accuracy	0.965066	0.414042
precision	1	0.953703
recall	1	0.000352897
mcc	0.930131	0.414042

	score	threshold
logloss	0.142383	nan
auc	0.983982	nan
f1	0.960699	0.351742
accuracy	0.960699	0.351742
precision	1	0.961723
recall	1	0.000341111
mcc	0.921397	0.351742

	score	threshold
logloss	0.230157	nan
auc	0.981751	nan
f1	0.960699	0.430041
accuracy	0.960699	0.430041
precision	1	0.816499
recall	1	0.000807625
mcc	0.921538	0.518465